As digital imaging becomes more commonplace, image processing applications that attempt to improve image quality have become more prevalent. According to an example, some image processing applications use information within an input image to enhance image quality of such input image. For instance, various filters can be applied to the input image to enhance image quality. According to another example, some image processing applications use information from one image to enhance image quality of another image. In accordance with an illustration, information from one image can be manually copied and pasted into a different image (e.g., a region of a first image can be manually copied and pasted into a region of a second image).
Still other conventional image processing techniques attempt to create a high-quality image from a low-quality image through inference. For instance, some conventional techniques estimate image information lost in a down-sampling process. These techniques commonly differ in how they model the high-resolution image.
Some conventional approaches model edge statistics, for example. These approaches can produce a higher-resolution image that has sharp edges and is generally smooth in other regions. Given an input image where edges can be identified, these approaches can produce a high-quality result with sharp edges (e.g., a Gaussian profile prior can be used to generate the high-quality result). However, the performance of the conventional approaches that model edge statistics can degrade for low-resolution input images. For instance, given a version of the same input image with reduced quality (e.g., where edges are unable to be identified), the result produced by the Gaussian profile prior can worsen due to lack of edges in the input image.